Abstract
The recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom-up (BU) process-based biogeochemical models and top-down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi-model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better-performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU- and TD-based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter-site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site-specific and ecosystem-specific variabilities inferred from observations.
| Original language | English |
|---|---|
| Pages (from-to) | 4298-4312 |
| Number of pages | 15 |
| Journal | Global Change Biology |
| Volume | 29 |
| Issue number | 15 |
| DOIs | |
| State | Published - Aug 2023 |
Funding
This study was funded by the RUBISCO SFA of the Regional and Global Modeling Analysis (RGMA)and the E3SM program in the U.S. Department of Energy Office of Science under contract DE-AC02-05CH11231. This work was also conducted as a part of the Wetland FLUXNET Synthesis for Methane Working Group supported by the John Wesley Powell Center for Analysis and Synthesis of the U.S. Geological Survey. The compilation of the FLUXNET-CH4 data is supported by the Gordon and Betty Moore Foundation through Grant GBMF5439 “Advancing Understanding of the Global Methane Cycle” to Stanford University supporting the Methane Budget activity for the Global Carbon Project (globalcarbonproject.org). We acknowledge the FLUXNET-CH4 community product (Delwiche et al., 2021) and Global Carbon Project CH4 modeling group (Saunois et al., 2020) for the data provided in this analysis. We thank Peter Bergamaschi for sharing the TM5-CAMS model data used in this study. FJ acknowledges support by the Swiss National Science Foundation (#200020_200511). FM and CP acknowledge the National Computational Infrastructure of the National Computational Infrastructure of the Australian Government through the NCMAS Allocation Scheme (grant NCMAS-2021-78), and the Sydney Informatics Hub HPC Allocation Scheme supported by the Office of the Deputy Vice-Chancellor (Research). N.G. acknowledges support from the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). This study was funded by the RUBISCO SFA of the Regional and Global Modeling Analysis (RGMA)and the E3SM program in the U.S. Department of Energy Office of Science under contract DE‐AC02‐05CH11231. This work was also conducted as a part of the Wetland FLUXNET Synthesis for Methane Working Group supported by the John Wesley Powell Center for Analysis and Synthesis of the U.S. Geological Survey. The compilation of the FLUXNET‐CH data is supported by the Gordon and Betty Moore Foundation through Grant GBMF5439 “Advancing Understanding of the Global Methane Cycle” to Stanford University supporting the Methane Budget activity for the Global Carbon Project ( globalcarbonproject.org ). We acknowledge the FLUXNET‐CH community product (Delwiche et al., 2021 ) and Global Carbon Project CH modeling group (Saunois et al., 2020 ) for the data provided in this analysis. We thank Peter Bergamaschi for sharing the TM5‐CAMS model data used in this study. FJ acknowledges support by the Swiss National Science Foundation (#200020_200511). FM and CP acknowledge the National Computational Infrastructure of the National Computational Infrastructure of the Australian Government through the NCMAS Allocation Scheme (grant NCMAS‐2021‐78), and the Sydney Informatics Hub HPC Allocation Scheme supported by the Office of the Deputy Vice‐Chancellor (Research). N.G. acknowledges support from the Newton Fund through the Met Office Climate Science for Service Partnership Brazil (CSSP Brazil). 4 4 4
| Funders | Funder number |
|---|---|
| Office of the Deputy Vice-Chancellor | |
| Office of the Deputy Vice‐Chancellor | |
| U.S. Geological Survey | |
| Gordon and Betty Moore Foundation | GBMF5439 |
| Office of Science | DE‐AC02‐05CH11231 |
| Newton Fund | |
| Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung | 200020_200511, NCMAS‐2021‐78 |
Keywords
- benchmarking
- bottom-up models
- eddy covariance
- methane emissions
- observational constraints
- top-down models
- wetland modeling
